Pleasure, quality or status? an analysis of drivers of purchase of fresh pork in China

1 Introduction

What are consumers aiming to get when they buy fresh meat? It is a widely accepted tenet in marketing that if a customer does not perceive value in a market offering, the customer is not likely to buy (Day and Wensley, 1988; Woodruff, 1997). As the concept of value has many meanings both in science and in everyday language (Loebler and Wloka, 2019), considerable effort has been made in exploring the concept of customer perceived value, from the simple notion that value is the perceived balance between what you get and what you have to give, to multi-dimensional conceptualizations of customer value, where different aspects of the gain component are distinguished (for example functional, emotional and social gains, see Zeithaml et al. (2020), for an overview). When customer perceived value becomes a multi-dimensional concept, the importance of the various dimensions for creating customer loyalty can differ, and these differences may be dependent on characteristics of both the market offering and the customer, a notion that has been amply supported by research (Swait and Sweeney, 2000; Leroi-Werelds et al., 2014). This also goes for value perception of fresh meat.

When consumers consistently perceive high value in a product, they may become loyal customers, buying the product again and again and praising it when talking to others. Loyalty as well is not unidimensional. It is common to distinguish between attitudinal and behavioral loyalty, and it has been shown that these two components do not always align (Dick and Basu, 1994). With both customer value and loyalty being multidimensional concepts, what if the way dimensions of customer value affect customer loyalty is different for attitudinal and behavioral loyalty? This question has received only scant attention in the literature, even though it is of obvious importance for practice. Purchases – the manifestation of behavioral loyalty – are the ultimate aim of all sellers, but without underlying attitudinal loyalty even repeated purchases may be unstable and easily influenced by, for example, competitor activities. When different dimensions of customer value affect attitudinal and behavioral loyalty, how can a seller ensure a strong positive attitude that also will translate into strong behavioral loyalty?

With multiple channels for sales, delivery and communication proliferating in the meat industry, the way customers perceive value can be linked to the multiples ways in which the customer can come into contact with the product and the brand over time. Every occasion in which the customer has such contact – for example, when seeing advertising, having the product discussed in social media, seeing it in the store – can be defined as a touchpoint between the customer and the product, and the sequence of touchpoints that the customer experiences over time is dubbed the customer journey. The concept of customer journey segments has been proposed in order to capture differences in customers’ use of these different touchpoints (Herhausen et al., 2019). Different touchpoints differ in their ability to convey information to the customer and in their ability to contribute to the value creation process, and will in many cases attract different types of customers. We therefore argue that the way in which dimensions of customer value perception are linked to attitudinal and behavior loyalty will differ between customer journey segments.

In the following, we present a study on how dimensions of customer perceived value for fresh pork meat are linked to attitudinal and behavior loyalty for different customer journey segments in China. Fresh pork in China is an interesting case for several reasons. The market for fresh pork in China is a good example of multi-channel marketing, with a multitude of brands being available across a range of channels ranging from brick-and-mortar stores via online channels to wet markets. Pork is a highly competitive market in China, with many brands competing for consumer demand, and is a frequently bought staple, which is interesting from the customer journey point of view. Most existing research on customer journeys is on services or durable products, where a journey across several touchpoints can be categorized into a pre-purchase, purchase and post-purchase phase (Lemon and Verhoef, 2016). This perspective is not applicable in relation to fast moving consumer goods like pork, where consumers are in almost continuous contact with a variety of touchpoints and where the customer journey becomes an ongoing process with single purchases being just elements in this process. Pork in China is a fast moving consumer good, but is still (in contrast to the situation in Europe or the USA) heavily, such that the consumer can get into contact with the brand on multiple occasions, for example in advertising and on social media. While mapping individual consumer journeys for a fast-moving consumer good like pork is difficult, the concept of customer journey segments, defined based on the patterns of channel usage, is a promising way of making the customer journey concept usable for such a product.

Our contribution to the literature is twofold. First, we add to the literature on attitudinal and behavioral loyalty by showing that discrepancies between the two can attributed to attitudinal and behavioral loyalty being affected by different components of customer perceived value. Third, we show how the concept of customer journey segments can be related to loyalty formation by invoking the customer value construct, responding to the Marketing Science Institute’s (2018) call for more research on sources of loyalty during the customer journey.

2 Conceptual development

2.1 Customer value

In a widely cited paper, Zeithaml (1988) defined customer perceived value (CPV) as the balance between the perceived gains and the perceived sacrifice linked to a market offering, a notion that has become widely adopted. The concept of customer perceived value has since been recognized as a cornerstone in understanding consumer behavior. The two-dimensional view of CPV has been developed into a multi-dimensional view, mostly based on distinguishing different types of gains. Sweeney and Soutar (2001), building on earlier work from Sheth et al. (1991), proposed a four-dimensional conceptualization of CPV, distinguishing the components emotional value, social value, quality/performance value and price/value for money. This conceptualization and the scale that has been developed for measuring it has been widely adopted in subsequent studies on CPV (Wang et al., 2004; Smith and Colgate, 2007; Papista and Krystallis, 2013; Hernandez-Ortega et al., 2017; Fazal-E-Hasan et al., 2018), although other multi-dimensional approaches have appeared in the literature (see Zeithaml et al. (2020), for an overview).

In our study, we adopt the concept of CPV of Sweeney and Soutar (2001) and investigate how the four dimensions of emotional value, social value, quality/performance value and price/value for money affect both attitudinal and behavioral customer loyalty to fresh pork brands. Important to note is that we validated that these four dimensions covered the main aspects of value perception for consumers in a Chinese context by conducting two preparatory focus groups. For example, one participant in these focus groups stated that one of the pork brands they knew was Economical, cost-effective, high-class meat quality, it is clean and makes you feel assured.

2.2 Attitudinal and behavioral loyalty

It seems intuitive that customer perceived value and customer loyalty should be related, although research has shown that this relationship is actually complex (Leroi-Werelds et al., 2014). For example, Floh et al. (2014) found that different dimensions of CPV are drivers of repurchase intention for different types of customers. In analyzing this relationship, it is important to address the distinction between attitudinal and behavioral loyalty. It has long been argued that loyalty as measured by repeated purchases may be based on ‘inertia’ rather than on a conviction of brand superiority (Assael, 1984). Dick and Basu (1994), in a widely cited contribution, have therefore argued that loyalty has two components, relative attitude and repeat patronage, and that these two need not be aligned. When they are not aligned, there can be cases of ‘latent loyalty’ (when attitude is positive but does not translate into repeat patronage) or of ‘spurious loyalty’ (when repeat purchases occur without being based on positive attitude). The existence of such cases of non-alignment has been demonstrated also for the grocery sector (Møller Jensen, 2011; Ngobo, 2017). Potential discrepancies between relative attitude and repeat patronage can be analyzed as a special case of the attitude-behavior gap (Boulstridge and Carrigan, 2000; Carrigan and Attalla, 2001; Sheeran, 2002; Auger et al., 2007; Papaoikonomou et al., 2011). This attitude-behavior gap has been clearly documented also with regard to food-related behaviors (for example for purchasing organic food, (Shepherd et al., 2005); environmentally friendly products (Moraes et al., 2012); or fair-trade foods (Chatzidakis et al., 2007). A number of reasons for this gap have been discussed in the literature, including the role of social norms (Fishbein and Ajzen, 1975), a lack of control over the behavior (Ajzen, 2002), the advent of unforeseen circumstances, or a lack of attitude activation at the time of the behavior, especially when the attitude is weakly grounded in a belief structure (Fazio et al., 1989).

We would like to argue that an additional possible reason for discrepancies between attitudinal and behavioral loyalty is that they are affected by different dimensions of customer perceived value. There is some patchy existing evidence to support this notion. Pura (2005), in a study on mobile services, found support for her hypotheses that different dimensions of CPV affect commitment and behavior intentions, although her hypotheses were specific to the service investigated. Also, there was no attempt at generalizations regarding which CPV dimensions would generally have more impact on the one or the other. Wang et al. (2004), studying an unspecified service, looked at how CPV dimensions related to, among other constructs, behavioral intentions and felt loyalty, and found that not all effects of CPV dimensions on behavioral intentions were completely mediated by felt loyalty.

Differential effects of CPV dimensions on attitudinal and behavioral loyalty may be related to attitude functions. Attitudes cannot only guide behavior but can also help define one’s identity (Maio and Olson, 1999; Briñol et al., 2019). If a consumer defines him/herself as quality conscious and thrifty, the quality/performance and price/value for money dimensions of CPV may have most influence on that consumer’s attitudinal loyalty, because it provides consistency with that person’s self-perception. Still, the emotional and social dimensions of CPV may have an impact on that person’s behavioral loyalty, based on the emotional and social gratification that these dimensions of CPV bring about.

2.3 Customer journey segments

Consumer decision-making and purchasing often occurs in a multi-channel setting, where consumers travel between different touchpoints where they encounter the products that they decide between. The sequence of these interactions is often called ‘the customer journey’ in the marketing literature (Lemon and Verhoef, 2016). For frequently bought consumer products like fresh pork, the customer journey will be expressed by the pattern of usage of different sales channels and other touchpoints where the consumer meets the competing brand across an ongoing sequence of purchases (Ieva and Ziliani, 2018). Consumers will differ in touchpoint usage during the customer journey, and these differences can be captured by distinguishing customer journey segments. Herhausen et al. (2019) showed that drivers of loyalty differed between customer journey segments, which they identified across a range of different product categories, though not including groceries, which they argued are still too much dominated by offline selling. We extend this research into the grocery sector in a market, China, where multi-channel selling of groceries is widespread, and provide additional insights by linking drivers of loyalty to the customer perceived value construct. For example, it could be the case that price/value for money is more important for those who shop more via online supermarkets vs. regular supermarkets, and that for those who shop in high-end supermarkets more than regular supermarkets quality and social recognition is more important.

2.4 Research context

Fresh pork in China is distributed through different sales channels, of which different variations of brick-and-mortar supermarkets and online retail stores are the most important, and in addition is heavily branded, allowing consumers to identify competing products across different channels and touchpoints. The Chinese food retailing system has changed quite drastically and rapidly during the past decades (Veeck and Veeck, 2000; Si et al., 2016). While the traditional wet markets still have a large share of fresh food retailing, other channels such as super- and hypermarkets are now dominant channels for purchasing fresh food (Veeck and Veeck, 2000; Si et al., 2016). Moreover, online purchasing channels have proliferated greatly in the last years and provide consumers with easy means of purchasing food and having it delivered to their doorsteps quickly (Maimaiti et al., 2018).

2.5 Hypotheses and conceptual model

This study aims to gain an understanding of how different dimensions of customer perceived value of pork affect attitudinal and behavioral loyalty for different customer journey segments. Overall, we hypothesize that:

● H1: The four dimensions of customer perceived value – emotional value, social value, quality/performance value and price/value for money – affect attitudinal loyalty.

● H2: The four dimensions of customer perceived value affect behavioral loyalty both directly and indirectly via attitudinal loyalty.

● H3: The pattern of direct influence of the four dimensions of perceived value on attitudinal loyalty differs from the pattern of direct influence of the four dimensions of perceived value on behavioral loyalty.

● H4: Different customer journey segments differ in the relationship between customer perceived value, attitudinal loyalty and behavioral loyalty.

See Figure 1 for a graphical presentation of our conceptual model.

Figure 1

Figure 1 Conceptual approach.

3 Materials and methods

3.1 Sample

Data were collected by means of an online survey with consumers in the 1^st tier cities Guangzhou, Shanghai, Shenzhen, and the 2^nd tier cities Hangzhou and Nanjing, n = 400 per city, resulting in a total sample size of 2000. Respondents were recruited through a major commercial consumer panel provider. Respondents were included if they were at least partly responsible for food shopping in the household and if they had bought pork at least once during the last month. Limiting the sampling to 1^st and 2^nd tier cities is meaningful as this is where purchasing power is concentrated, and this is where multiple channels in the distribution of pork are most clearly visible.

3.2 Measures

Brand awareness for eleven major brands of fresh pork in China was measured by asking the participants which of these 11 pork brands they had seen previously. This was used as a filter for measuring customer perceived value, attitudinal loyalty and behavior loyalty. Buying frequency for each of these brands was measured by asking participants how frequently they bought any of these brands in their last 10 purchases of pork meat. Out of all the brands that participants were familiar with, participants were asked questions about customer perceived value, attitudinal and behavioral loyalty for two randomly selected brands.

Customer perceived value was measured for the four dimensions: quality/performance, price/value for money, emotional value and social value with items adapted from Sweeney and Soutar (2001), e.g., brand X has consistent quality or brand X offers value for money rated on a 1-7 Likert scale (‘strongly disagree’ – ‘strongly agree’). The items and their loadings can be seen in Table 1.

Table 1

Table 1 Measurement model for PLS analysis (n=4000).

Attitudinal loyalty was measured with four items, e.g., Purchasing meat from brand X is … where participants could rate the answers on a 1-7 scale ranging from e.g., harmful (1) to beneficial (7); see Table 1.

In this study, we measured behavioral loyalty in two different ways: First, we measured future purchase intention. This was measured with the question In the future, when I buy pork, brand X will be the brand I buy most often on a 1-7 scale (‘not at all likely’ – ‘very likely’). Secondly, we measured whether respondents had one particular brand that they bought more often than any other brand during their last 10 pork purchases. We coded this as follows: If one brand was chosen more frequently than all other brands, then this was listed as the respondent’s favorite (e.g., a brand was coded as a consumer’s ‘favorite brand’ if it was bought most frequently, for example 6 times out of the last 10, whereas the remaining 4 choices were made for several other different brands). However, if several brands were chosen equally frequently (e.g., multiple brands being chosen twice in the last 10 purchases, yet none being chosen more than twice), then this respondent was classified as having ‘no favorite’.

Channel usage patterns. In relation to channel use, consumers were asked to think of the last ten times that they purchased pork meat. For each of these purchases, respondents had to select from a list of common shopping channels where they had bought the pork: mainstream supermarkets, wet markets, convenience stores, high-end supermarkets, online stores, and imported-goods stores. The responses thus show how the last ten purchases were distributed across the different channels. We use these data to identify customer journey segments.

4 Results

Below, we will first elaborate on the sample composition and the reliability of our measures. This is followed by our segmentation analysis and the investigation of the effects of customer perceived value on attitudinal and behavioral loyalty.

4.1 Sample composition and reliability of measures

We collected data from 2000 participants. The sample consisted of 70% females, indicating that there are still more females than males doing the household shopping. All demographic characteristics of the sample can be seen in Table 2.

Table 2

Table 2 Sample characteristics.

We also computed Cronbach’s α for the measures for customer perceived value and attitudinal loyalty. For CPV we found that all four dimensions had good reliability (perceived quality= .87; value for money= .85; expected enjoyment= .83; social recognition= .89). Attitudinal loyalty (.75) also had good reliability.

4.2 Identification of customer journey segments

We identified segments according to differences in their channel usage. Most respondents reported buying pork at a mainstream supermarket (94%) at least 1 out of 10 purchases, but differed in the frequency with which they used other channels. Respondents were grouped according to their channel usage by performing a latent class analysis in LatentGold (Vermunt and Magidson, 2013). A four-cluster solution was adopted as a compromise between analysis of the information criteria AIC and BIC and interpretability.

A large segment of consumers buys most frequently at mainstream supermarkets, and does not use other channels very frequently, except for the wet markets. We termed this group the ‘mainstream buyers’, and they make up for 46% of our sample. The second segment was termed ‘online buyers’ as the people in this segment use online channels much more frequently than the other groups (16% of our respondents). Third, there is a consumer group who purchase using all channels, which we named ‘channel nomads’ (17% of our respondents). Finally, there is a group of consumers, who next to mainstream supermarkets buy most frequently in high-end supermarkets, convenience stores and imported goods stores, which are arguably more upscale channels than wet markets or online, and thus we termed this group the ‘high-end buyers’. This final cluster makes up 21% of our respondents (see Figure 2).

Figure 2

Figure 2 Customer journey segments.

4.3 Effect of customer perceived value on attitudinal and behavioral loyalty

In order to analyze the effect of the four dimensions of CPV on attitudinal and behavioral loyalty, we performed a partial least squares structural equation model (PLS-SEM) analysis in SmartPLS 3 (Ringle et al., 2015). The structural model consisted of six correlated constructs (quality/performance, emotional value, social value, price/value for money, attitudinal loyalty, and behavioral loyalty). The convergent validity of our measurement model was acceptable (i.e., the average variance extracted, AVE, is larger than.5). as our AVEs ranged from.54-.75. Moreover, the Cronbach’s alphas, as reported previously, are all satisfactory, as are all values for Jöreskog’s composite reliability (>.82). With regard to discriminant validity, the heterotrait-monotrait (HTMT) ratio of the correlations should be under.90 for conceptually similar constructs, which was the case in our study (ranging from.32 to.89). The complete measurement model can be seen in Table 1.

As PLS-SEM is a nonparametric method, bootstrapping is required to determine statistical significance of the path coefficients (Hair et al., 2019). In our study, we used a bootstrapping of 5000. The results of the model for the full sample can be found in Table 3.

Table 3

Table 3 Direct, indirect and total effects table for model on full sample and segment-specific subsamples.

When looking at the effect of dimensions of CPV on attitudinal and behavioral loyalty, we see that perceived quality/performance is a positive predictor of attitudinal loyalty, but does not directly predict behavioral loyalty. Price/value for money and emotional value also have a direct positive influence on attitudinal loyalty. Social value had a small - but negative - influence on attitudinal loyalty. Overall, three out of four dimensions of PCV have a strong positive effect on attitudinal loyalty. Furthermore, we find the same pattern for the indirect effects of all four PCV measures on behavioral loyalty, mediated by attitudinal loyalty.

Additionally, price/value for money is a stronger predictor for behavioral than for attitudinal loyalty. Moreover, social value is a positive predictor for behavioral loyalty, yet a negative predictor for attitudinal loyalty. All three results combined give a clear indication that attitudinal and behavioral loyalty indeed have different determinants. This will be further elaborated on in the discussion section.

We then investigated if these relationships differ for the customer journey segments distinguished based on their channel usage. In order to do this, we conducted a multi-group path analysis using SmartPLS. The results for the different segments can also be found in Table 3. We found the following significant differences between the path coefficients for the customer journey segments: High-end buyers differ from mainstream buyers in that emotional value was a larger predictor of behavioral loyalty for mainstream buyers than for high-end buyers. Moreover, quality/performance was a significant predictor of behavioral loyalty only for the high-end buyers, making them significantly different from the mainstream and online buyers (but not from channel nomads). Finally, channel nomads were significantly different from mainstream buyers as well as online buyers in the relation between attitudinal and behavioral loyalty, as for mainstream and online buyers’ attitudinal loyalty is a stronger predictor of behavioral loyalty than for channel nomads. This may hint towards channel nomads also being more likely brand nomads. This will be addressed in the following section.

4.4 Customer journey segments and behavioral loyalty

When looking at current behavioral loyalty, the results show that one third of the respondents did not have a favorite brand, meaning that current behavioral loyalty for pork meat in China is rather low. We investigated if the four consumer segments distinguished earlier differ in terms of their patterns of preferred brands, see Figure 3. The results show that the distribution of purchases on different brands differs between consumer segments: channel nomads purchased 5.5 different brands on average in their last 10 purchases, the online buyers 4.1, the high-end buyers 3.8 and the mainstream buyers 3.3. Interesting to note, however, is that the channel nomads are the ones who do not favor one brand over another the most, meaning that they are indeed also most likely to be ‘brand nomads’. This may be partly explained due to the smaller effect that attitudinal loyalty has in predicting behavioral loyalty.

Figure 3

Figure 3 Behavioural loyalty, measured as brand most frequently purchased, by customer journey segment.

5 Discussion

In this study we investigated how different dimensions of customer perceived value for fresh pork affect attitudinal and behavioral loyalty and how this differs between customer journey segments. Overall, we found support for our four hypotheses: the four dimensions of CPV, quality/performance value, emotional value, social value and price/value for money, were predictors of both attitudinal and behavioral loyalty, but the pattern of influence was different between attitudinal and behavioral loyalty. In addition, the patterns differed between different customer journey segments.

5.1 Discussion of findings

We looked at four dimensions of CPV: quality/performance, price/value for money, emotional value and social value. We find that quality/performance is the strongest predictor of attitudinal loyalty, whereas expected emotional value is the strongest predictor of behavioral loyalty. Further, the effect of quality/performance on behavioral loyalty is fully mediated by attitudinal loyalty, whereas the other three dimensions of CPV have both direct and indirect effects on behavioral loyalty. Overall, this confirms our expectation that the four dimensions of CPV affect attitudinal and behavioral loyalty differently, strengthening the preliminary evidence provided by Pura (2005) and Wang et al. (2004).

We may interpret these findings based on theories of attitude functions (Shavitt and Nelson, 2002). Attitude to the brand may in this case primarily serve a self-assertive function, reinforcing consumers’ self-perception of being a quality conscious and thrifty buyer. This does to some extent translate to behavioral loyalty, but in addition behavioral loyalty is affected by the perceived emotional value of the product, which becomes the dominant driver of behavioral loyalty. That affective reactions can affect purchase intention for a meat product besides or on top of its quality evaluation has been shown before (Saeed and Grunert, 2014).

Another interesting finding was that the perceived social value had a negative direct effect on attitudinal loyalty, yet a positive direct effect on behavioral loyalty. Social value pertains to the social recognition that people perceive being linked to using the product. It is thus close to the construct of social norm, which has been widely used to explain gaps in the link between attitude and behavior, also with regard to food (e.g., Vermeir & Verbeke, 2006). Hence, our results are in line with the common finding of a positive effect of perceived social pressure or encouragement on behavior and behavioral intentions. In addition to that, however, our findings suggest that buying a meat brand because of the perceived social recognition coming with it, is something that detracts from the attitude to the brand. A possible explanation for this is because buying a brand for such a reason is not in line with one’s self-perception as an autonomous decision-maker, which again would be in line with the view of attitude as having a self-expressive function.

We also looked at how different patterns of customer journeys across different channels may affect the way in which CPV affects attitudinal and behavioral loyalty. Our results show that the pattern of influence of dimensions of CPV on attitudinal and behavioral loyalty differs between the segments. Two aspects are worth emphasizing. First, the overall finding that quality/performance is the strongest direct predictor of attitudinal loyalty, whereas emotional value is the strongest predictor of behavioral loyalty, holds for most of the customer journey segments, but not for the high end buyers, where the effect of these two dimensions on behavioral loyalty is about equal, which is in good correspondence with the defining criterion for these customers, namely that they shop a lot in high-end outlets. Second, the ambivalent effects of social value discussed above work differently in the different segments. The negative effect of social value on attitudinal loyalty is found only for the mainstream buyers segment. The positive effect of social value on behavioral loyalty occurs only for high end buyers and for channel nomads. Thus, the segments react differently on the perception of social value of the different brands.

Our results have some implications for loyalty building on the market for fresh pork in China. China is unique in their heavy branding of fresh meat, whereas in most Western countries fresh meat is sold unbranded or under retailer labels. Still, brand loyalty of the Chinese consumers with regard to pork brands appears to be low. This could mirror the fact that, apart from a distinction of some premium brands mostly distinguished by the use of a particular pig race (black pigs), brand differentiation is rather low, with most brands having similar brand positioning based on safety and good taste (Pedersen et al., 2020). Our results underline the importance of creating customer value both in terms of quality and in terms of emotional benefits. In addition, our results indicate that the use of different channels has an effect on brand image; therefore, ensuring consistent brand encounters across the different channels may be very important.

5.2 Limitations and future research

Some limitations should be considered when interpreting the results of this study. Most importantly, this is a single country study and the results therefore do not easily generalize. Future research could experimentally investigate the effect of different value propositions, defined in terms of the dimensions of CPV distinguished here, on attitudinal and behavioral loyalty. This could be combined with measures of attitude function in order to be able to test the soundness of our interpretation that attitude function is a major factor in explaining these differential effects.

Second, the investigation is based on a cross-sectional online survey, which means that interpretations in terms of causality should be made with caution. A third limitation is that all behaviors measured in this study are based on self-report and not on direct observations.

5.3 Conclusion

We conclude that buying fresh meat is, for Chinese consumers, mostly driven by the expected pleasure and to a lesser degree by quality and functional properties like safety and healthiness. The latter do have an impact on consumers’ attitude to the product, but less so on their buying behavior, suggesting that attitude and purchase are driven by different mental processes. Moreover, we find that these relationships differ between different segments of consumers distinguished based on their customer journey patterns.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Aarhus University Research Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

MM: Conceptualization, Formal Analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. KG: Conceptualization, Formal Analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing. SP: Conceptualization, Methodology, Project administration, Writing – review & editing. KB: Conceptualization, Data curation, Writing – review & editing. YZ: Conceptualization, Data curation, Project administration, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study has received financial support from the Danish Agricultural and Food Council.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Ajzen I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior 1. J. Appl. Soc. Psychol. 32, 665–683. doi: 10.1111/j.1559-1816.2002.tb00236.x

CrossRef Full Text | Google Scholar

Assael H. (1984). Consumer behavior and market action. Boston MA: Kent.

Google Scholar

Auger P., Devinney T. M., Louviere J. J. (2007). Using best–worst scaling methodology to investigate consumer ethical beliefs across countries. J. business ethics 70, 299–326. doi: 10.1007/s10551-006-9112-7

CrossRef Full Text | Google Scholar

Boulstridge E., Carrigan M. (2000). Do consumers really care about corporate responsibility? Highlighting the attitude—behaviour gap. J. communication Manage. 4, 355–368. doi: 10.1108/eb023532

CrossRef Full Text | Google Scholar

Briñol P., Petty R. E., Stavraki M. (2019). “Structure and function of attitudes,” in Oxford research encyclopedia of psychology. (Oxford: Oxford University Press).

Google Scholar

Carrigan M., Attalla A. (2001). The myth of the ethical consumer–do ethics matter in purchase behaviour? J. consumer marketing 18, 560–578. doi: 10.1108/07363760110410263

CrossRef Full Text | Google Scholar

Chatzidakis A., Hibbert S., Smith A. P. (2007). Why people don’t take their concerns about fair trade to the supermarket: The role of neutralisation. J. business ethics 74, 89–100. doi: 10.1007/s10551-006-9222-2

CrossRef Full Text | Google Scholar

Day G. S., Wensley R. (1988). Assessing advantage: a framework for diagnosing competitive superiority. J. marketing 52, 1–20. doi: 10.1177/002224298805200201

CrossRef Full Text | Google Scholar

Dick A. S., Basu K. (1994). Customer loyalty: toward an integrated conceptual framework. J. Acad. marketing Sci. 22, 99–113. doi: 10.1177/0092070394222001

CrossRef Full Text | Google Scholar

Fazal-E-Hasan S. M., Ahmadi H., Mortimer G., Grimmer M., Kelly L. (2018). Examining the role of consumer hope in explaining the impact of perceived brand value on customer–brand relationship outcomes in an online retailing environment. J. Retailing Consumer Serv. 41, 101–111. doi: 10.1016/j.jretconser.2017.12.004

CrossRef Full Text | Google Scholar

Fazio R. H., Powell M. C., Williams C. J. (1989). The role of attitude accessibility in the attitude-to-behavior process. J. consumer Res. 16, 280–288. doi: 10.1086/209214

CrossRef Full Text | Google Scholar

Fishbein M., Ajzen I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. (Reading, MA: Addison-Wesley).

Google Scholar

Floh A., Zauner A., Koller M., Rusch T. (2014). Customer segmentation using unobserved heterogeneity in the perceived-value–loyalty–intentions link. J. Business Res. 67, 974–982. doi: 10.1016/j.jbusres.2013.08.003

CrossRef Full Text | Google Scholar

Hair J. F., Risher J. J., Sarstedt M., Ringle C. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus Rev. 26, 106–121.

Google Scholar

Herhausen D., Kleinlercher K., Verhoef P. C., Emrich O., Rudolph T. (2019). Loyalty formation for different customer journey segments. J. Retailing 95, 9–29. doi: 10.1016/j.jretai.2019.05.001

CrossRef Full Text | Google Scholar

Hernandez-Ortega B., Aldas-Manzano J., Ruiz-Mafe C., Sanz-Blas S. (2017). Perceived value of advanced mobile messaging services: A cross-cultural comparison of Greek and Spanish users. Inf. Technol. People 30, 324–355. doi: 10.1108/ITP-01-2014-0017

CrossRef Full Text | Google Scholar

Ieva M., Ziliani C. (2018). Mapping touchpoint exposure in retailing: Implications for developing an omnichannel customer experience. Int. J. Retail Distribution Manage. 46, 304–322. doi: 10.1108/IJRDM-04-2017-0097

CrossRef Full Text | Google Scholar

Lemon K. N., Verhoef P. C. (2016). Understanding customer experience throughout the customer journey. J. marketing 80, 69–96. doi: 10.1509/jm.15.0420

CrossRef Full Text | Google Scholar

Leroi-Werelds S., Streukens S., Brady M. K., Swinnen G. (2014). Assessing the value of commonly used methods for measuring customer value: A multi-setting empirical study. J. Acad. marketing Sci. 42, 430–451. doi: 10.1007/s11747-013-0363-4

CrossRef Full Text | Google Scholar

Loebler H., Wloka M. (2019). Customers’ everyday understanding of ‘value’from a second-order cybernetic perspective. J. Marketing Manage. 35, 992–1014. doi: 10.1080/0267257X.2019.1632374

CrossRef Full Text | Google Scholar

Maimaiti M., Zhao X., Jia M., Ru Y., Zhu S. (2018). How we eat determines what we become: opportunities and challenges brought by food delivery industry in a changing world in China. Eur. J. Clinical Nut. 72, 1282–1286.

Google Scholar

Maio G. R., Olson J. M. (1999). Why we evaluate: Functions of attitudes. (London: Psychology Press).

Google Scholar

Marketing Science Institute. (2018). Research Priorities 2018-2020. Cambridge, MA: Marketing Science Institute.

Google Scholar

Møller Jensen J. (2011). Consumer loyalty on the grocery product market: an empirical application of Dick and Basu's framework. J. Consumer Marketing 28, 333–343. doi: 10.1108/07363761111149983

CrossRef Full Text | Google Scholar

Moraes C., Carrigan M., Szmigin I. (2012). The coherence of inconsistencies: Attitude–behaviour gaps and new consumption communities. J. Marketing Manage. 28, 103–128. doi: 10.1080/0267257X.2011.615482

CrossRef Full Text | Google Scholar

Ngobo P. V. (2017). The trajectory of customer loyalty: an empirical test of Dick and Basu’s loyalty framework. J. Acad. Marketing Sci. 45, 229–250. doi: 10.1007/s11747-016-0493-6

CrossRef Full Text | Google Scholar

Papaoikonomou E., Ryan G., Ginieis M. (2011). Towards a holistic approach of the attitude behaviour gap in ethical consumer behaviours: Empirical evidence from Spain. Int. Adv. Economic Res. 17, 77–88. doi: 10.1007/s11294-010-9288-6

CrossRef Full Text | Google Scholar

Papista E., Krystallis A. (2013). Investigating the types of value and cost of green brands: Proposition of a conceptual framework. J. business ethics 115, 75–92. doi: 10.1007/s10551-012-1367-6

CrossRef Full Text | Google Scholar

Pedersen S., Grunert K. G., Zhou Y. (2020). PORKBRAND WP1: Mapping Present Sales Channels for Pork in China. Available at: https://mgmt.au.dk/fileadmin/Business_Administration/MAPP/Porkbrand_WP1_report.pdf.

Google Scholar

Pura M. (2005). Linking perceived value and loyalty in location-based mobile services. Managing Service Quality: Int. J. 15, 509–538. doi: 10.1108/09604520510634005

CrossRef Full Text | Google Scholar

Ringle C. M., Wende S., Becker J. M. (2015). SmartPLS 3. (Bönningstedt: SmartPLS). Available at: http://www.smartpls.com.

Google Scholar

Saeed F., Grunert K. G. (2014). Expected and experienced quality as predictors of intention to purchase four new processed beef products. Br. Food J. 116, 451–471. doi: 10.1108/BFJ-10-2011-0262

CrossRef Full Text | Google Scholar

Shavitt S., Nelson M. R. (2002). The role of attitude functions in persuasion and social judgment. In: Dillar J. P., Shen L. (Eds.). The Sage Handbook of Persuasive Communication. (Thousand Oaks, CA: Sage). 137–153.

Google Scholar

Sheeran P. (2002). Intention—behavior relations: a conceptual and empirical review. Eur. Rev. Soc. Psychol. 12, 1–36. doi: 10.1080/14792772143000003

CrossRef Full Text | Google Scholar

Shepherd R., Magnusson M., Sjödén P.-O. (2005). Determinants of consumer behavior related to organic foods. AMBIO: A J. Hum. Environ. 34, 352–359. doi: 10.1579/0044-7447-34.4.352

CrossRef Full Text | Google Scholar

Sheth J. N., Newman B. I., Gross B. L. (1991). Why we buy what we buy: A theory of consumption values. J. business Res. 22, 159–170. doi: 10.1016/0148-2963(91)90050-8

CrossRef Full Text | Google Scholar

Si Z., Scott S., McCordic C. (2016). Supermarkets, Wet Markets and Food Patronage in Nanjing, China. Hungry Cities Partnership Report).

Google Scholar

Smith J. B., Colgate M. (2007). Customer value creation: a practical framework. J. marketing Theory Pract. 15, 7–23. doi: 10.2753/MTP1069-6679150101

CrossRef Full Text | Google Scholar

Swait J., Sweeney J. C. (2000). Perceived value and its impact on choice behavior in a retail setting. J. Retailing Consumer Serv. 7, 77–88. doi: 10.1016/S0969-6989(99)00012-0

CrossRef Full Text | Google Scholar

Sweeney J. C., Soutar G. N. (2001). Consumer perceived value: The development of a multiple item scale. J. retailing 77, 203–220. doi: 10.1016/S0022-4359(01)00041-0

CrossRef Full Text | Google Scholar

Veeck A., Veeck G. (2000). Consumer segmentation and changing food purchase patterns in Nanjing, PRC. World Development. 28, 457–471.

Google Scholar

Vermunt J. K., Magidson J. (2013). Technical Guide for Latent GOLD 5.0: Basic, Advanced, and Syntax. (Belmont, MA: Statistical Innovations Inc).

Google Scholar

Wang Y., Po Lo H., Chi R., Yang Y. (2004). An integrated framework for customer value and customer-relationship-management performance: a customer-based perspective from China. Managing Service Quality: Int. J. 14, 169–182. doi: 10.1108/09604520410528590

CrossRef Full Text | Google Scholar

Woodruff R. B. (1997). Customer value: the next source for competitive advantage. J. Acad. marketing Sci. 25, 139–153. doi: 10.1007/BF02894350

CrossRef Full Text | Google Scholar

Zeithaml V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. J. marketing 52, 2–22. doi: 10.1177/002224298805200302

CrossRef Full Text | Google Scholar

Zeithaml V. A., Verleye K., Hatak I., Koller M., Zauner A. (2020). Three decades of customer value research: paradigmatic roots and future research avenues. J. Service Res. 23, 409–432. doi: 10.1177/1094670520948134

CrossRef Full Text | Google Scholar